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Minor improvement (RandAug)

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Harle, Antoine (Contracteur) 2020-01-30 11:21:25 -05:00
parent 6bba069d8a
commit 561b71b30a
5 changed files with 50 additions and 179 deletions
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32
higher/smart_aug/dataug.py
View file

@ -187,11 +187,11 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
Ensure that the parameters value stays in the right intevals. This should be called after each update of those parameters.
Args:
soft (bool): Wether to use a softmax function for TF probabilites. Not Recommended as it tends to lock the probabilities, preventing them to be learned. (default: False)
soft (bool): Wether to use a softmax function for TF probabilites. Tends to lock the probabilities if the learning rate is low, preventing them to be learned. (default: False)
"""
if not self._fixed_prob:
if soft :
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0)
else:
self._params['prob'].data = self._params['prob'].data.clamp(min=1/(self._nb_tf*100),max=1.0)
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
@ -269,6 +269,14 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
"""
self._data_augmentation=mode
def is_augmenting(self):
""" Return wether data augmentation is applied.
Returns:
bool : True if data augmentation is applied.
"""
return self._data_augmentation
def __getitem__(self, key):
"""Access to the learnable parameters
Args:
@ -588,6 +596,14 @@ class Data_augV7(nn.Module): #Proba sequentielles
"""
self._data_augmentation=mode
def is_augmenting(self):
""" Return wether data augmentation is applied.
Returns:
bool : True if data augmentation is applied.
"""
return self._data_augmentation
def __getitem__(self, key):
"""Access to the learnable parameters
Args:
@ -659,6 +675,8 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
})
self._shared_mag = True
self._fixed_mag = True
self._fixed_prob=True
self._fixed_mix=True
self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
@ -753,6 +771,14 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
"""
self._data_augmentation=mode
def is_augmenting(self):
""" Return wether data augmentation is applied.
Returns:
bool : True if data augmentation is applied.
"""
return self._data_augmentation
def __getitem__(self, key):
"""Access to the learnable parameters
Args:
@ -796,7 +822,7 @@ class Higher_model(nn.Module):
"""
super(Higher_model, self).__init__()
self._name = model.__str__()
self._name = model.__class__.__name__ #model.__str__()
self._mods = nn.ModuleDict({
'original': model,
'functional': higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)

163
higher/smart_aug/test_brutus.py
View file

@ -1,163 +0,0 @@
from model import *
from dataug import *
#from utils import *
from train_utils import *
tf_names = [
## Geometric TF ##
'Identity',
'FlipUD',
'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
]
device = torch.device('cuda')
if device == torch.device('cpu'):
device_name = 'CPU'
else:
device_name = torch.cuda.get_device_name(device)
##########################################
if __name__ == "__main__":
n_inner_iter = 1
epochs = 150
dataug_epoch_start=0
optim_param={
'Meta':{
'optim':'Adam',
'lr':1e-2, #1e-2
},
'Inner':{
'optim': 'SGD',
'lr':1e-1, #1e-2
'momentum':0.9, #0.9
}
}
#model = LeNet(3,10)
#model = ResNet(num_classes=10)
#model = MobileNetV2(num_classes=10)
#model = WideResNet(num_classes=10, wrn_size=32)
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
####
'''
t0 = time.process_time()
aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
exec_time=time.process_time() - t0
####
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
'''
####
'''
t0 = time.process_time()
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
exec_time=time.process_time() - t0
####
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
'''
res_folder="../res/brutus-tests2/"
epochs= 150
inner_its = [1]
dist_mix = [0.0, 0.5, 0.8, 1.0]
dataug_epoch_starts= [0]
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [4, 3, 2]
mag_setup = [(True,True), (False, False)] #(Fixed, Shared)
#prob_setup = [True, False]
nb_run= 3
try:
os.mkdir(res_folder)
os.mkdir(res_folder+"log/")
except FileExistsError:
pass
for n_inner_iter in inner_its:
for dataug_epoch_start in dataug_epoch_starts:
for n_tf in N_seq_TF:
for dist in dist_mix:
#for i in TF_nb:
for m_setup in mag_setup:
#for p_setup in prob_setup:
p_setup=False
for run in range(nb_run):
if (n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True)) or (p_setup and dist!=0.0): continue #Autres setup inutiles sans meta-opti
#keys = list(TF.TF_dict.keys())[0:i]
#ntf_dict = {k: TF.TF_dict[k] for k in keys}
t0 = time.process_time()
model = ResNet(num_classes=10)
model = Higher_model(model) #run_dist_dataugV3
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=50,
KLdiv=True)
exec_time=time.process_time() - t0
####
print('-'*9)
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs (dataug:{})- {} in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter, run)
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
except:
print("Failed to save logs :",f.name)
try:
plot_resV2(log, fig_name="../res/"+filename, param_names=aug_model.TF_names())
except:
print("Failed to plot res")
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
#'''

25
higher/smart_aug/test_dataug.py
View file

@ -53,10 +53,6 @@ tf_names = [
#'Random',
#'RandBlend'
#Non fonctionnel
#'Auto_Contrast', #Pas opti pour des batch (Super lent)
#'Equalize',
]
@ -67,6 +63,12 @@ if device == torch.device('cpu'):
else:
device_name = torch.cuda.get_device_name(device)
torch.backends.cudnn.benchmark = True #Faster if same input size #Not recommended for reproductibility
#Increase reproductibility
torch.manual_seed(0)
np.random.seed(0)
##########################################
if __name__ == "__main__":
@ -78,7 +80,7 @@ if __name__ == "__main__":
}
#Parameters
n_inner_iter = 1
epochs = 1
epochs = 150
dataug_epoch_start=0
optim_param={
'Meta':{
@ -95,9 +97,8 @@ if __name__ == "__main__":
#Models
model = LeNet(3,10)
#model = ResNet(num_classes=10)
#Lents
#model = MobileNetV2(num_classes=10)
#model = WideResNet(num_classes=10, wrn_size=32)
#import torchvision.models as models
#model=models.resnet18()
#### Classic ####
if 'classic' in tasks:
@ -105,7 +106,7 @@ if __name__ == "__main__":
model = model.to(device)
print("{} on {} for {} epochs".format(str(model), device_name, epochs))
log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=1)
log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=20)
#log= train_classic_higher(model=model, epochs=epochs)
exec_time=time.process_time() - t0
@ -130,11 +131,10 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
model = Higher_model(model) #run_dist_dataugV3
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
log= run_simple_smartaug(model=aug_model, epochs=epochs, inner_it=n_inner_iter, opt_param=optim_param)
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,
@ -142,7 +142,8 @@ if __name__ == "__main__":
opt_param=optim_param,
print_freq=1,
unsup_loss=1,
hp_opt=False)
hp_opt=False,
save_sample_freq=None)
exec_time=time.process_time() - t0
####

8
higher/smart_aug/train_utils.py
View file

@ -287,13 +287,19 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
diffopt.detach_()
model['model'].detach_()
meta_opt.zero_grad()
elif not high_grad_track:
diffopt.detach_()
model['model'].detach_()
tf = time.process_time()
if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
try:
viz_sample_data(imgs=xs, labels=ys, fig_name='../samples/data_sample_epoch{}_noTF'.format(epoch))
model.train()
viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='../samples/data_sample_epoch{}'.format(epoch))
model.eval()
except:
print("Couldn't save samples epoch"+epoch)
pass
@ -315,9 +321,9 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
"acc": accuracy,
"time": tf - t0,
"mix_dist": model['data_aug']['mix_dist'].item(),
"param": param,
}
if not model['data_aug']._fixed_mix: data["mix_dist"]=model['data_aug']['mix_dist'].item()
if hp_opt : data["opt_param"]=[{'lr': p_grp['lr'].item(), 'momentum': p_grp['momentum'].item()} for p_grp in diffopt.param_groups]
log.append(data)
#############

1
higher/smart_aug/utils.py
View file

@ -131,6 +131,7 @@ def viz_sample_data(imgs, labels, fig_name='data_sample', weight_labels=None):
fig_name (string): Relative path where to save the graph. (default: data_sample)
weight_labels (Tensor): Weights associated to each labels. (default: None)
"""
sample = imgs[0:25,].permute(0, 2, 3, 1).squeeze().cpu()
plt.figure(figsize=(10,10))